What is continual learning in deep learning?

Continual learning in deep learning refers to training models to learn new tasks or data over time without forgetting previously acquired knowledge. Unlike traditional approaches where a model is trained once on a fixed dataset, continual learning systems adapt incrementally as new data or tasks become available. For example, a model trained to recognize cats might later learn to recognize dogs without losing its ability to identify cats. This is critical in real-world scenarios where data arrives sequentially (e.g., user preferences in an app, new sensor data in robotics) and retraining from scratch is impractical.

A key challenge in continual learning is catastrophic forgetting, where a model overwrites important weights learned from previous tasks when training on new data. This occurs because neural networks optimize for the current task, often disregarding past information. For instance, if a model trained on medical images for disease A is later fine-tuned for disease B, it might perform poorly on disease A unless precautions are taken. Another challenge is balancing stability (retaining old knowledge) and plasticity (adapting to new data). Methods must ensure the model doesn’t become rigid or overly specialized to recent tasks while avoiding interference with prior learning.

Several strategies address these challenges. Regularization-based approaches, like Elastic Weight Consolidation (EWC), penalize changes to weights deemed important for previous tasks. Architectural methods, such as progressive networks, dynamically expand the model’s structure to accommodate new tasks without altering existing components. Rehearsal techniques store a subset of old data or generate synthetic samples to replay during training on new tasks. For example, a self-driving car system might use rehearsal to retain rare but critical scenarios (e.g., pedestrian detection) while learning new road conditions. These approaches aim to strike a balance between efficiency, scalability, and performance, making continual learning viable for applications like personalized AI assistants or adaptive industrial systems.